Brief communication: An innovation-based estimation method for model error covariance in Kalman filters

We present a simple innovation-based model error covariance estimation method for Kalman filters. The method is based on Berry and Sauer (2013) and the simplification results from assuming known observation error covariance. We carry out experiments with a prescribed model error covariance using a Lorenz (1996) model and ensemble Kalman filter. The prescribed error covariance matrix is recovered with high accuracy.

Robust recovery of sparse non-negative weights from mixtures of positive-semi-definite matrices

We consider a structured estimation problem where an observed matrix is assumed to be generated as an $s$-sparse linear combination of $N$ given $n\times n$ positive-semi-definite matrices. Recovering the unknown $N$-dimensional and $s$-sparse weights from noisy observations is an important problem in various fields of signal processing and also a relevant preprocessing step in covariance estimation. We will present related recovery guarantees and focus on the case of non-negative weights. The problem is formulated as a convex program and can be solved without further tuning. Such robust, non-Bayesian and parameter-free approaches are important for applications where prior distributions and further model parameters are unknown. Motivated by explicit applications in wireless communication, we will consider the particular rank-one case, where the known matrices are outer products of iid. zero-mean sub-Gaussian $n$-dimensional complex vectors. We show that, for given $n$ and $N$, one can recover non-negative $s$-sparse weights with a parameter-free convex program once $s\leq O(n^2 / \log ^2(N/n^2)$. Our error estimate scales linearly in the instantaneous noise power whereby the convex algorithm does not need prior bounds on the noise. Such estimates are important if the magnitude of the additive distortion depends on the unknown itself.

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals

For an airborne passive radar with contaminated reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the covariance estimation in space-time adaptive processing (STAP). In order to overcome the severe STAP performance degradation caused by impure reference signals and off-grid effects, a novel MP clutter suppression method based on local search is proposed for airborne passive radar. In the proposed method, the global dictionary is constructed based on the sparse measurement model of MP clutter, and the global atoms that are most relevant to the residual are selected. Then, the local dictionary is designed iteratively, and local searches are performed to match real MP clutter points. Finally, the off-grid effects are mitigated, and the MP clutter is suppressed from all matched atoms. A range of simulations is conducted in order to demonstrate the effectiveness of the proposed method.

AbacusSummit: A Massive Set of High-Accuracy, High-Resolution N-Body Simulations

We present the public data release of the AbacusSummit cosmological N-body simulation suite, produced with the Abacus N-body code on the Summit supercomputer of the Oak Ridge Leadership Computing Facility. Abacus achieves $\mathcal {O}\left(10^{-5}\right)$ median fractional force error at superlative speeds, calculating 70M particle updates per second per node at early times, and 45M particle updates per second per node at late times. The simulation suite totals roughly 60 trillion particles, the core of which is a set of 139 simulations with particle mass 2 × 109 h−1 M⊙ in box size 2 h−1 Gpc. The suite spans 97 cosmological models, including Planck 2018, previous flagship simulation cosmologies, and a linear derivative and cosmic emulator grid. A sub-suite of 1883 boxes of size 500 h−1 Mpc is available for covariance estimation. AbacusSummit data products span 33 epochs from z = 8 to 0.1 and include lightcones, full particle snapshots, halo catalogs, and particle subsets sampled consistently across redshift. AbacusSummit is the largest high-accuracy cosmological N-body data set produced to date.